Transforming brain research with AI and machine learning
Using machine learning, researchers are able to use data from the brain to glean deeper insights and apply this new knowledge in clinical settings. The findings will be presented on Monday, November 13, 2–3 p.m. EST at Neuroscience 2023, the annual meeting of the Society for Neuroscience and the world’s largest source of emerging news about brain science and health.
Machine learning is a branch of artificial intelligence (AI) that centers on enabling computers to analyze data in increasingly complex ways. Researchers use algorithms with adaptive models that can change and develop without outside intervention. In neuroscience research, machine learning can study large datasets with information from the human brain and also apply models to predict outcomes based on that information.
New findings show that:
- Researchers can successfully identify activity between the thalamus and motor-related regions as correlated with depressive symptoms, applying neuroimaging techniques to detect abnormal brain regions in psychiatric disorders. (Ayumu Yamashita, Advanced Telecommunications Research Institute International)
- Machine learning models using patient data can predict progression from mild cognitive impairment (MCI)to Alzheimer’s disease (AD); their study focuses on the use of kPCA (kernel Principal Components Analysis). (Nikita Goel, University of Southern California)
- A new application of artificial neural networks can uncover how cells in the visual cortex interpret light signals from the retina and translate that data into vision. (Dan Butts, University of Maryland)
- Researchers applied machine learning models to deep brain recordings and combined that with traditional control theory methods to create a tool for predicting targets for deep brain stimulation (DBS). This optimization can help with treatment for dystonia in children. (Maral Kasiri, University of California, Irvine)
“Advances in AI and machine learning are transforming brain research and clinical treatments,” said Terry Sejnowski, the Francis Crick Professor at the Salk Institute for Biological Studies and distinguished professor at UC San Diego, who will moderate the press conference. “Brain recordings produce huge datasets that can be analyzed with machine learning. Predictive modeling, machine-brain interfaces, and neuroimaging/neuromodulation are areas with particular promise in developing new therapeutics and treatment plans for patients.”
This research was supported by national funding agencies including the National Institutes of Health and private funding organizations. Find out more about AI and brain research on BrainFacts.org.
Monday, November 13, 2023
2–3 p.m. EST
Walter E. Washington Convention Center, Room 202B
Press conference summary
- These presentations explore the wealth of data that can be extracted and analyzed using machine learning. These topics range from predictive modeling that identifies brain activity for disorders such as Alzheimer’s disease (AD) and depression (the first two studies) to analyzing the brain’s complex visual system and applying models of deep brain networks for advancing therapies (the third and fourth studies, respectively).
Unsupervised-based feature selection method robustly extracted resting state functional connectivity related to major depressive disorder
Ayumu Yamashita, [email protected], Abstract PSTR099.15
- Researchers used neuroimaging techniques combined with machine learning to identify brain biomarkers. To determine how accurately and robustly these techniques detect abnormal brain regions in psychiatric disorders, the researchers used three types of feature selection methods with regard to major depressive disorder (MDD).
- Out of the three methods, they found that the unsupervised-based feature selection method robustly extracted functional connections with larger effect sizes in the test data. Using MRI data from 1,162 patients, including 334 with depression, researchers extracted coordinated activity between parts of the brain (especially the thalamus and motor-related regions) that are correlated with depressive symptoms.
Predicting future decline from mild cognitive impairment to Alzheimer’s disease with machine learning and 3D brain MRI
Nikita Goel, [email protected], Abstract NANO03.07
- Predictive models use machine learning to try to determine who will develop brain disorders such as AD. Researchers used a range of models to see what model might predict mild cognitive impairment (MCI) that would progress to AD.
- The researchers used MRI scans along with demographic and genetic data from more than 2,448 people. The most functional model they developed used kPCA (kernel principal components analysis) to generate new features by combining the scan data with data about an AD risk gene as well as basic patient data.
Biologically constrained deep neural networks to parse visual computations being performed in the primary visual cortex
Dan Butts, [email protected], Abstract PSTR149.16
- Researchers used a novel application of artificial neural networks called convolutional deep neural networks (CNNs) to learn how cells in the visual cortex of the brain interpret light signals captured by the retina.
- Using machine learning, researchers are beginning to understand how information about the visual world is carried across brain cells in the visual cortex.
Identifying the effective targets for deep brain stimulation: system identification using a simple recurrent neural network
Maral Kasiri, [email protected], Abstract PSTR154.16
- Clinicians face challenges with deep brain stimulation (DBS) in determining the optimal targets within the brain. In this way, treatment can be tailored to a specific patient to better treat movement disorder symptoms.
- Based on intrinsic brain signals of brain areas (i.e., basal ganglia and thalamus) in children with dystonia, machine learning can be applied to develop models of deep brain networks combined with traditional control theory and statistical methods. The end result is a tool to identify the most favorable DBS targets for patients.
